منابع مشابه
The Probability of Backtest Overfitting
Many investment firms and portfolio managers rely on backtests (i.e., simulations of performance based on historical market data) to select investment strategies and allocate capital. Standard statistical techniques designed to prevent regression overfitting, such as holdout, tend to be unreliable and inaccurate in the context of investment backtests. We propose a general framework to assess th...
متن کاملOnline tools for demonstration of backtest overfitting
In mathematical finance, backtest overfitting means the usage of historical market data (a backtest) to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data available. Backtest overfitting is now thought to be a primary reason why quantitative investment models and strategies that look good on paper often disappoint in practice. In ...
متن کاملStock portfolio design and backtest overfitting
In mathematical finance, backtest overfitting connotes the usage of historical market data to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data available. Backtest overfitting is now thought to be a primary reason why investment models and strategies that look good on paper often disappoint in practice. Models and strategies suff...
متن کاملPseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance
Introduction A backtest is a historical simulation of an algorithmic investment strategy. Among other things, it computes the series of profits and losses that such strategy would have generated had that algorithm been run over that time period. Popular performance statistics, such as the Sharpe ratio or the Information ratio, are used to quantify the backtested strategy’s return on risk. Inves...
متن کاملThe Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-normality
With the advent in recent years of large financial data sets, machine learning and highperformance computing, analysts can backtest millions (if not billions) of alternative investment strategies. Backtest optimizers search for combinations of parameters that maximize the simulated historical performance of a strategy, leading to backtest overfitting. The problem of performance inflation extend...
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ژورنال
عنوان ژورنال: The Journal of Computational Finance
سال: 2016
ISSN: 1460-1559
DOI: 10.21314/jcf.2016.322